Data Discovery and Security: Protecting Sensitive Information

Abstract

The growth of contemporary computer hardware and the availability of soft-computing tools have penetrated the market and influenced prospective research across nearly all areas of education. Management education is not exempt from the growing trends examined by experts recently. Nonetheless, it is a truth that the bulk of research, innovation, and development predominantly concentrate on the fundamental disciplines of science and engineering, either for parametric analysis or for addressing real-world problems. There is significant opportunity for study utilizing empirical methodologies in Management Education. In the context of effective governance, particularly within Public Administration where citizen-centric choices are made, opportunities for prospective study were observed. Consequently, a study is conducted to test the integration of developing technologies and to illustrate its potential connection to practical applications in decision-making systems. The research outcomes, both contemporary and historical, have predominantly concentrated on parametric studies, and solutions derived from study for real-world practical issues are infrequent. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) are a developing computational paradigm inspired by the human brain's functionality, generating significant interest in modeling complex behavioral issues in recent times. This effort attempts to establish a fresh connection between AI and many specific administrative applications. The primary impetus for researchers to examine, study, and employ the knowledge acquisition approach known as neural computing is the accessibility of high-speed digital computers, robust software/languages, and contemporary ideas of machine learning and brain processing. Current and historical literature indicates significant potential for the advancement of modern techniques, such as artificial neural networks, which may address complicated real-world issues that are otherwise challenging and costly to represent analytically or by direct computing. This research aims to address several complicated administrative and management issues that have not been explored inside the realm of AI. Practical examples are chosen from the extensive range of governance systems.